Book : Practical Machine Learning For Computer Vision...

Descripción

- ANTES DE COMPRAR PREGUNTE FECHA DE ENTREGA.
- ENVIAMOS POR MERCADOENVIOS
- PUEDE RETIRAR POR AHORA SOLO POR QUILMES, MICROCENTRO ESTA CERRADO, POR ESO...
- EN CABA (CAPITAL FEDERAL) ENVIAMOS SIN CARGO ESTE PRODUCTO.
- FORMA DE PAGO : MERCADOPAGO
- HACEMOS FACTURA A.
- ELBAZARDIGITAL VENDEDOR PLATINUM
- TODOS NUESTROS PRODUCTOS EN:

https://eshops.mercadolibre.com.ar/elbazardigital

-X-X-X-

- SOMOS IMPORTADORES DIRECTOS, ESTE PRODUCTO SE COMPRA Y SE IMPORTA DESDE ESTADOS UNIDOS, ESTO IMPLICA QUE USTED ESTA COMPRANDO EL MISMO PRODUCTO QUE COMPRARÍA UN CLIENTE DE ESE PAÍS.

- ANTES DE REALIZAR UNA CONSULTA, VISUALICE TODAS LAS IMAGENES DEL PRODUCTO.
Descripción provista por la editorial :

This practical book shows you how to employ machine learning models to extract information from images. ML engineers and data scientists will learn how to solve a variety of image problems including classification, object detection, autoencoders, image generation, counting, and captioning with proven ML techniques. This book provides a great introduction to end-to-end deep learning: dataset creation, data preprocessing, model design, model training, evaluation, deployment, and interpretability.Google engineers Valliappa Lakshmanan, Martin Gorner, and Ryan Gillard show you how to develop accurate and explainable computer vision ML models and put them into large-scale production using robust ML architecture in a flexible and maintainable way. Youll learn how to design, train, evaluate, and predict with models written in TensorFlow or Keras.Youll learn how to:Design ML architecture for computer vision tasksSelect a model (such as ResNet, SqueezeNet, or EfficientNet) appropriate to your taskCreate an end-to-end ML pipeline to train, evaluate, deploy, and explain your modelPreprocess images for data augmentation and to support learnabilityIncorporate explainability and responsible AI best practicesDeploy image models as web services or on edge devicesMonitor and manage ML models About the Author Valliappa (Lak) Lakshmanan is the director of analytics and AI solutions at Google Cloud, where he leads a team building cross-industry solutions to business problems. His mission is to democratize machine learning so that it can be done by anyone anywhere.Martin Gorner is a product manager for Keras/TensorFlow focused on improving the developer experience when using state-of-the-art models. Hes passionate about science, technology, coding, algorithms, and everything in between.Ryan Gillard is an AI engineer in Google Clouds Professional Services organization, where he builds ML models for a wide variety of industries. He started his career as a research scientist in the hospital and healthcare industry. With degrees in neuroscience and physics, he loves working at the intersection of those disciplines exploring intelligence through mathematics.
-o-o-o-

Garantía del vendedor: 90 días